Table REF  shows an experiment on Market-1501 to analyze the feature invariance of pre-trained models. The original dataset is extended into six variations by applying six different augmentations to each image, to simulate some important appearance variances in the ReID task (examples shown in Figure REF ). Then, Centered Kernel Alignment (CKA) [1]} scores can evaluate the similarity between the features of the origin dataset and the features of the each simulated dataset. The higher CKA score is, the features provided by the pre-trained model show better invariance on this type of augmentation. ViT\(_I\) -S achieves the best CKA scores for all types of appearance variance, showing that ICS is beneficial for learning invariant features.
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